Data deduplication, as a compression method, has been widely used in most backup systems to improve bandwidth and space efficiency. As data exploded to be backed up, two main challenges in data deduplication are the C...Data deduplication, as a compression method, has been widely used in most backup systems to improve bandwidth and space efficiency. As data exploded to be backed up, two main challenges in data deduplication are the CPU-intensive chunking and hashing works and the I/0 intensive disk-index access latency. However, CPU-intensive works have been vastly parallelized and speeded up by multi-core and many-core processors; the I/0 latency is likely becoming the bottleneck in data deduplication. To alleviate the challenge of I/0 latency in multi-core systems, multi-threaded deduplication （Multi-Dedup） architecture was proposed. The main idea of Multi-Dedup was using parallel deduplication threads to hide the I/0 latency. A prefix based concurrent index was designed to maintain the internal consistency of the deduplication index with low synchronization overhead. On the other hand, a collisionless cache array was also designed to preserve locality and similarity within the parallel threads. In various real-world datasets experiments, Multi-Dedup achieves 3-5 times performance improvements incorporating with locality-based ChunkStash and local-similarity based SiLo methods. In addition, Multi-Dedup has dramatically decreased the synchronization overhead and achieves 1.5-2 times performance improvements comparing to traditional lock-based synchronization methods.展开更多
In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive fun...In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive functional control(AFPFC) scheme for multivariable nonlinear systems was proposed.Firstly,multivariable nonlinear systems were described based on Takagi-Sugeno(T-S) fuzzy models;assuming that the antecedent parameters of T-S models were kept,the consequent parameters were identified on-line by using the weighted recursive least square(WRLS) method.Secondly,the identified T-S models were linearized to be time-varying state space model at each sampling instant.Finally,by using linear predictive control technique the analysis solution of the optimal control law of AFPFC was established.The application results for pH neutralization process show that the absolute error between the identified T-S model output and the process output is smaller than 0.015;the tracking ability of the proposed AFPFC is superior to that of non-AFPFC(NAFPFC) for pH process without disturbances,the overshoot of the effluent pH value of AFPFC with disturbances is decreased by 50% compared with that of NAFPFC;when the process parameters of AFPFC vary with time the integrated absolute error(IAE) performance index still retains to be less than 200 compared with that of NAFPFC.展开更多
基金Project(IRT0725)supported by the Changjiang Innovative Group of Ministry of Education,China
文摘Data deduplication, as a compression method, has been widely used in most backup systems to improve bandwidth and space efficiency. As data exploded to be backed up, two main challenges in data deduplication are the CPU-intensive chunking and hashing works and the I/0 intensive disk-index access latency. However, CPU-intensive works have been vastly parallelized and speeded up by multi-core and many-core processors; the I/0 latency is likely becoming the bottleneck in data deduplication. To alleviate the challenge of I/0 latency in multi-core systems, multi-threaded deduplication （Multi-Dedup） architecture was proposed. The main idea of Multi-Dedup was using parallel deduplication threads to hide the I/0 latency. A prefix based concurrent index was designed to maintain the internal consistency of the deduplication index with low synchronization overhead. On the other hand, a collisionless cache array was also designed to preserve locality and similarity within the parallel threads. In various real-world datasets experiments, Multi-Dedup achieves 3-5 times performance improvements incorporating with locality-based ChunkStash and local-similarity based SiLo methods. In addition, Multi-Dedup has dramatically decreased the synchronization overhead and achieves 1.5-2 times performance improvements comparing to traditional lock-based synchronization methods.
基金Project(2007AA04Z162) supported by the National High-Tech Research and Development Program of ChinaProjects(2006T089, 2009T062) supported by the University Innovation Team in the Educational Department of Liaoning Province, China
文摘In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive functional control(AFPFC) scheme for multivariable nonlinear systems was proposed.Firstly,multivariable nonlinear systems were described based on Takagi-Sugeno(T-S) fuzzy models;assuming that the antecedent parameters of T-S models were kept,the consequent parameters were identified on-line by using the weighted recursive least square(WRLS) method.Secondly,the identified T-S models were linearized to be time-varying state space model at each sampling instant.Finally,by using linear predictive control technique the analysis solution of the optimal control law of AFPFC was established.The application results for pH neutralization process show that the absolute error between the identified T-S model output and the process output is smaller than 0.015;the tracking ability of the proposed AFPFC is superior to that of non-AFPFC(NAFPFC) for pH process without disturbances,the overshoot of the effluent pH value of AFPFC with disturbances is decreased by 50% compared with that of NAFPFC;when the process parameters of AFPFC vary with time the integrated absolute error(IAE) performance index still retains to be less than 200 compared with that of NAFPFC.
